Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Naimi, Safwen"'
To extract robust and generalizable skeleton action recognition features, large amounts of well-curated data are typically required, which is a challenging task hindered by annotation and computation costs. Therefore, unsupervised representation lear
Externí odkaz:
http://arxiv.org/abs/2409.05749
This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The
Externí odkaz:
http://arxiv.org/abs/2311.03177
Autor:
Naimi, Safwen, Koubaa, Olfa, Bouachir, Wassim, Bilodeau, Guillaume-Alexandre, Jeddore, Gregory, Baines, Patricia, Correia, David, Arsenault, Andre
Lichens are symbiotic organisms composed of fungi, algae, and/or cyanobacteria that thrive in a variety of environments. They play important roles in carbon and nitrogen cycling, and contribute directly and indirectly to biodiversity. Ecologists typi
Externí odkaz:
http://arxiv.org/abs/2310.17080
In this paper, we propose a novel deep learning method based on a new Hybrid ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD) from gait data. We adopt a two-step approach by dividing the problem into two sub-problems. Our
Externí odkaz:
http://arxiv.org/abs/2310.17078
Supervised learning can learn large representational spaces, which are crucial for handling difficult learning tasks. However, due to the design of the model, classical image classification approaches struggle to generalize to new problems and new si
Externí odkaz:
http://arxiv.org/abs/2111.04845
Autor:
Naimi, Safwen1 (AUTHOR) safwen.naimi@teluq.ca, Bouachir, Wassim1 (AUTHOR), Bilodeau, Guillaume-Alexandre2 (AUTHOR)
Publikováno v:
Neural Computing & Applications. Feb2024, Vol. 36 Issue 4, p1947-1957. 11p.
